class_docstr: Tests the correctness of the Y = g(X) = Log[1 + exp(X)] transformation.
code: def testBijectiveAndFinite32bit(self):
      bijector = softplus.Softplus()
      x = np.linspace(-20.0, 20.0, 100).astype(np.float32)
      y = np.logspace(-10, 10, 100).astype(np.float32)
      bijector_test_util.assert_bijective_and_finite(
          bijector,
          x,
          y,
          eval_func=self.evaluate,
          event_ndims=0,
          rtol=1e-2,
          atol=1e-2,
      )
  
imports: from absl.testing import parameterized
  import numpy
  import tensorflow.compat.v2
  from tensorflow_probability.python.bijectors import bijector_test_util
  from tensorflow_probability.python.bijectors import softplus
  from tensorflow_probability.python.internal import test_util
  from tensorflow_probability.python.math import gradient
label_desc: Use image node_id 15 for calling the SoftplusBijectorTest obj's underlying member method code with example usage: obj.testBijectiveAndFinite32bit() without return types